Facade-Manager: Digital Twin Manage System for Glass Facade Leveraging Multimodal Large Model

Managing glass facade through unmanned and intel-ligent methods has become a crucial demand in city management. However, traditional methods fail to adequately convey the status of glass facades and lack clear standards for assessment. To address these issues, we propose a novel method that combines digital twin with multi-modal large model for the intelligent and unmanned management of glass facades. More specifically, we first define comprehensive information presentation requirements for glass facades maintenance, including the facade's appearance, IoT device status, and robot positioning, addressed through image-based 3D modeling, cloud integration, and infrared motion capture. Second, we utilize multi-modal large model fine-tuning and prompt learning to assimilate maintenance knowledge, en-hancing decision-making and planning for maintenance tasks, with outputs standardized to integrate seamlessly with the system. Finally, we develop a digital twin manage system which offers robust, reliable management of glass facades with a user-friendly interface. Real-world evaluation confirmed its excellence in information collection and response accuracy, validating its reliability and effectiveness in intelligent facade maintenance.